Our framework applies to a wide range of SMF-type problems for multi-class classification with auxiliary features.

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The field of learning-augmented online algorithms has gained rapid prominence in recent years.

We asked attendees of Super Bowl LX's pregame festivities for their takes on the competing halftime shows, the potential for ICE actions, and the influence of Silicon Valley on the event. To say this year's Super Bowl came at a charged time in American culture and politics is, perhaps, an understatement. While the pair of teams who took the field Sunday--the Seattle Seahawks and the New England Patriots--comprised a pretty classic matchup (no underdogs here!), the rest of the event was set to be anything but. Santa Clara's Levi's Stadium is in the heart of Silicon Valley, just a few miles from the corporate headquarters of Nvidia and AMD, whose chips are powering the AI arms race that had competitors OpenAI and Anthropic sparring via rival Super Bowl ads . There was an explosion in sports "trading" activity on sites like Kalshi and Polymarket in the lead-up to the game, even in states like California where traditional sports betting is illegal. Sunday could prove to be an extraordinary success for prediction markets, as the industry becomes more mainstream . Fresh off a historic Grammy Album of the Year win (a first for a Spanish-language album), the unapologetically political Puerto Rican rapper and singer Bad Bunny headlined --a choice that sparked a perhaps inevitable MAGA backlash. Meanwhile, Turning Point USA organized an alternative program called The All-American Halftime Show, featuring the likes of Kid Rock and Brantley Gilbert. Never mind that Bad Bunny is Puerto Rican, and therefore an American citizen. Rumors were even buzzing about possible actions by US Immigration and Customs Enforcement agents at the Super Bowl. Even though the NFL and California governor Gavin Newsom said on Thursday that there would be " no immigration enforcement tied to the game," anti-ICE protesters were on the streets. We caught up with football fans at a tailgate five minutes away from Levi's Stadium to hear their thoughts on all the drama. Here's what they had to say.

'Nature seems to have already solved the problem.' Breakthroughs, discoveries, and DIY tips sent six days a week. To help figure out what makes stingrays such unique and unusual swimmers, a team of mechanical engineers at the University of California, Riverside (UCR) created a wavy robotic fin. After submerging the robot in underwater tunnels designed to mimic swimming near the sea floor, their tests indicate that different types of ray species may have evolved alternative swimming techniques that best suit their setting. Specifically, the findings suggest that some ray species swimming near the seafloor adjust the way their fins move and tilt to counter a downward force that would otherwise pull them toward the ground. It turns out that stingrays gracefully gliding along waves near seabeds aren't doing it to look cool.

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This paper studies networked multi-agent reinforcement learning (NMARL) with interdependent rewards and coupled policies. In this setting, each agent's reward depends on its own state-action pair as well as those of its direct neighbors, and each agent's policy is parameterized by its local parameters together with those of its $κ_{p}$-hop neighbors, with $κ_{p}\geq 1$ denoting the coupled radius. The objective of the agents is to collaboratively optimize their policies to maximize the discounted average cumulative reward. To address the challenge of interdependent policies in collaborative optimization, we introduce a novel concept termed the neighbors' averaged $Q$-function and derive a new expression for the coupled policy gradient. Based on these theoretical foundations, we develop a distributed scalable coupled policy (DSCP) algorithm, where each agent relies only on the state-action pairs of its $κ_{p}$-hop neighbors and the rewards of its $(κ_{p}+1)$-hop neighbors. Specially, in the DSCP algorithm, we employ a geometric 2-horizon sampling method that does not require storing a full $Q$-table to obtain an unbiased estimate of the coupled policy gradient. Moreover, each agent interacts exclusively with its direct neighbors to obtain accurate policy parameters, while maintaining local estimates of other agents' parameters to execute its local policy and collect samples for optimization. These estimates and policy parameters are updated via a push-sum protocol, enabling distributed coordination of policy updates across the network. We prove that the joint policy produced by the proposed algorithm converges to a first-order stationary point of the objective function. Finally, the effectiveness of DSCP algorithm is demonstrated through simulations in a robot path planning environment, showing clear improvement over state-of-the-art methods.

As artificial intelligence (AI) becomes embedded in personal and professional relationships, a new kind of power imbalance emerges from asymmetric memory capabilities. Human relationships have historically relied on mutual forgetting, the natural tendency for both parties to forget details over time, as a foundation for psychological safety, forgiveness, and identity change. By contrast, AI systems can record, store, and recombine interaction histories at scale, often indefinitely. We introduce Memory Power Asymmetry (MPA): a structural power imbalance that arises when one relationship partner (typically an AI-enabled firm) possesses a substantially superior capacity to record, retain, retrieve, and integrate the shared history of the relationship, and can selectively deploy that history in ways the other partner (the human) cannot. Drawing on research in human memory, power-dependence theory, AI architecture, and consumer vulnerability, we develop a conceptual framework with four dimensions of MPA (persistence, accuracy, accessibility, integration) and four mechanisms by which memory asymmetry is translated into power (strategic memory deployment, narrative control, dependence asymmetry, vulnerability accumulation). We theorize downstream consequences at individual, relational/firm, and societal levels, formulate boundary-conditioned propositions, and articulate six design principles for restoring a healthier balance of memory in human-AI relationships (e.g., forgetting by design, contextual containment, symmetric access to records). Our analysis positions MPA as a distinct construct relative to information asymmetry, privacy, surveillance, and customer relationship management, and argues that protecting mutual forgetting, or at least mutual control over memory, should become a central design and policy goal in the AI age.